Co-Ops Data Scientist- Statistics
Co-Ops Data Scientist
Global Data & Analytics Group is a cross-functional team within the Process Research group at Guardian Glass. We are on the front lines of projects that impact the operations and business of Guardian Industries manufacturing assets. We employ Machine Learning, Artificial intelligence, Optimization, Process Engineering, and IoT technologies to improve the efficiency of Guardian’s manufacturing processes. Examples of the challenges our group tackles are opportunities to optimize the existing operations, work closely with process research and operations teams to determine what process settings drive certain manufacturing results, and improve operations or predict future behaviors of a product as it moves through the manufacturing. GD&A group is that focuses on applying advanced quantitative methods to solve a wide variety of challenging operations problems.
As a Data Scientist will join a growing team dedicated to identifying, developing, refining, and deploying data science solutions in glassmaking, coating processes, and business forecasting. As a data scientist, you will use machine learning techniques to gain and share new insights, develop predictive tools, and improve process control and predictability. You will be responsible for working closely with key stakeholders and data engineers to efficiently identify and implement improvements to data infrastructure for downstream analytics needs. you will determine the right models to use for various process applications from ideation, proof-of-concept through the design of applications, as well as monitor and improve deployed operational-real-time models. You will keep Guardian current with state-of-the-art machine learning software and hardware in the cloud and on-premise and pursue continuous self-training and train others across the organization. You will gain exposure to senior leadership as you showcase your results to key stakeholders in the business, demonstrating the superior value of your work. You will report to the R&D Group Leader of Process Research & Data Analytics.
Our group of highly skilled, enthusiastic data scientists and operations research engineers is redefining what's possible for Guardian by utilizing the massive amount of process data to transform operations and research and development. We invest in our team by encouraging attendance at industry conferences and ongoing education opportunities enabling them to stay on the dynamic ever-changing data science landscape and bring new methods and techniques to their projects.
- Develop a strong understanding of the operations and business processes and identify possible opportunities that add value.
- Apply machine learning algorithms to perform analysis, create predictive models, visualize data, and drive projects through to delivery toward the solution of operations problems.
- Interact with business and manufacturing SMEs to identify requirements and propose solutions.
- Have the intellectual curiosity to research and identify new modeling technologies/methodologies/software packages to improve the current modeling processes
- Continuously scan and test new data sources, tools, and analytical techniques and partner with leading institutions and experts to contribute to our portfolio of next-generation analytics projects.
- Interface with key stakeholders and technical experts to ensure hardware and software support for data analytics is maintained
- Analyze large complex time series datasets to extract useful patterns to deliver business insights and communicate results to key decision-makers
- Issue reports detailing tool development (including unsuccessful approaches), quality assurance actions performed, results, etc.
- Work with data engineers to improve functionality in data systems (e.g., data reliability, efficiency, usability, and quality) and improve downstream data analysis capabilities
- Work with various functional teams; owning end-to-end solution development and scaling focused on operations challenges.
- Rapidly design, prototype, and test many possible hypotheses. Further, focus on building minimum viable products toward solving most of the issue instead of “perfecting” the solution, unless critical to safety.
- Engage with internal customers - operations teams and process SME’s - to leverage your critical thinking skills to apply data science modeling solutions.
- Research and implement advanced Machine Learning techniques to solve operation and business opportunities; move from proof of concept to minimum viable product efficiently.
- Leading and working on multiple projects at the same time and switching priorities as the business needs require.
- Employ visualization, reporting, and other tools to improve how teams access various datasets.
- Strong data-driven storytelling to present to stockholders.
·Pursing MS or Ph.D. in a quantitative field (Statistics, Mathematics, Operational Research, Economics or equivalent)
·Experience with various statistical analyses and modeling (Hypothesis testing, Regression analysis parametric and non-parametric, Multivariate, Time Series, DOE, etc.) and experience with its applications.
·Experience with predictive modeling and machine learning algorithms (classification, predictive, artificial neural networks, etc.)
·Experience with Python/R and data science-related libraries (StatsModels, Tensorflow/PyTorch, Keras, Scikit-learn, Pandas, Numpy, Plotly, Dash, Streamlit, ggplot2, plotnine, etc.)
·Strong data cleaning, transformation, and manipulation skills.
·Experience with data visualization
·Strong communication, problem-solving, and time management skills.
·Passion and curiosity to learn new technologies in a timely fashion.
·Strong data-driven storytelling to present to stockholders.
·Combination of analytical rigor and statistical methods with strong and creative problem-solving skills.
·Familiarity with various computing infrastructures and technologies e.g., AWS, GPU, Linux, GIT, etc.
·Working knowledge of glass manufacturing processes and/or vacuum coating
·Experience in process operations analytics and manufacturing optimization
·Knowledge of tools such as Spark, Hadoop, Pig, Kafka, Kinesis, Docker, Snowflake, Amazon SageMaker, AWS, Azure
·Knowledge in FinTech
·Experience with UI/UX.
·Fluent with SQL.
The position may require travel in the range of 10%.